---
# id: openai
title: OpenAI
sidebar_label: OpenAI
---

By default, DeepEval uses `gpt-4.1` to power all of its evaluation metrics. To enable this, you’ll need to set up your OpenAI API key. DeepEval also supports all other OpenAI models, which can be configured directly in Python.

### Setting Up Your API Key

DeepEval autoloads `.env.local` then `.env` at import time (process env -> `.env.local` -> `.env`).

**Recommended (local dev):**
```bash
# .env.local
OPENAI_API_KEY=<your-openai-api-key>
```

Alternative (Shell/CI)
```bash
export OPENAI_API_KEY=<your-openai-api-key>
```

Alternative (notebook)

If you're working in a notebook environment (Jupyter or Colab), set your `OPENAI_API_KEY` in a cell:

```bash
%env OPENAI_API_KEY=<your-openai-api-key>
```

### Command Line

Run the following command in your CLI to specify an OpenAI model to power all metrics.

```bash
deepeval set-openai \
    --model=gpt-4.1
    --cost_per_input_token=0.000002
    --cost_per_output_token=0.000008
```

:::info
The CLI command above sets `gpt-4.1` as the default model for all metrics, unless overridden in Python code. To use a different default model provider, you must first unset the current settings:

```bash
deepeval unset-openai
```

:::

:::tip Persisting settings
You can persist CLI settings with the optional `--save` flag.
See [Flags and Configs -> Persisting CLI settings](/docs/evaluation-flags-and-configs#persisting-cli-settings-with---save).
:::

### Python

You may use OpenAI models other than `gpt-4.1`, which can be configured directly in python code through DeepEval's `GPTModel`.

:::info
You may want to use stronger reasoning models like `gpt-4.1` for metrics that require a high level of reasoning — for example, a custom GEval for mathematical correctness.
:::

```python
from deepeval.models import GPTModel
from deepeval.metrics import AnswerRelevancyMetric

model = GPTModel(
    model="gpt-4.1",
    temperature=0,
    cost_per_input_token=0.000002,
    cost_per_output_token=0.000008
)
answer_relevancy = AnswerRelevancyMetric(model=model)
```

There are **ONE** mandatory and **ONE** optional parameters when creating a `GPTModel`:

- `model`: A string specifying the name of the GPT model to use. Defaulted to `gpt-4o`.
- [Optional] `temperature`: A float specifying the model temperature. Defaulted to 0.
- [Optional] `cost_per_input_token`: A float specifying the cost for each input token for the provided model.
- [Optional] `cost_per_output_token`: A float specifying the cost for each output token for the provided model.
- [Optional] `generation_kwargs`: A dictionary of additional generation parameters supported by your model provider.

:::info
You can use custom providers by setting `_openai_api_key` and `base_url` with your custom provider's details.
:::

:::tip
Any `**kwargs` you would like to use for your model can be passed through the `generation_kwargs` parameter. However, we request you to double check the params supported by the model and your model provider in their [official docs](https://platform.openai.com/docs/api-reference/responses/create).
:::

### Available OpenAI Models

:::note
This list only displays some of the available models. For a comprehensive list, refer to the OpenAI's official documentation.
:::

Below is a list of commonly used OpenAI models:

- `gpt-5`
- `gpt-5-mini`
- `gpt-5-nano`
- `gpt-4.1`
- `gpt-4.5-preview`
- `gpt-4o`
- `gpt-4o-mini`
- `o1`
- `o1-pro`
- `o1-mini`
- `o3-mini`
- `gpt-4-turbo`
- `gpt-4`
- `gpt-4-32k`
- `gpt-3.5-turbo`
- `gpt-3.5-turbo-instruct`
- `gpt-3.5-turbo-16k-0613`
- `davinci-002`
- `babbage-002`
